Bibliography

2023

1. 

   Convergence and error analysis of PINNs

   Nathan Doumèche, Gérard Biau, and Claire Boyer

   arXiv, 2023

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2. 

   Training neural operators to preserve invariant measures of chaotic attractors

   Ruoxi Jiang, Peter Y Lu, Elena Orlova, and 1 more author

   In Advances in Neural Information Processing Systems (NeurIPS), 2023

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3. 

   Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

   Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, and 2 more authors

   arXiv, 2023

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2022

2021

1. 

   Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism

   L. McClenny, and U. Braga-Neto

   In AAAI Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, 2021

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2020

1. 

   MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

   Chiyu Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, and 6 more authors

   In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2020

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2. 

   NeuPDE: Neural network based ordinary and partial differential equations for modeling time-dependent data

   Yifan Sun, Linan Zhang, and Hayden Schaeffer

   In Mathematical and Scientific Machine Learning, 2020

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2019

1. 

   Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

   M. Raissi, P. Perdikaris, and G.E. Karniadakis

   Journal of Computational Physics, 2019

2018